A model of destination competitiveness/sustainability: Brazilian perspectives
Bibliographic record
Abstract
This paper reviews the understanding I have gained from several years of research, and from several more years of ongoing discussions with industry leaders regarding the nature of competitiveness among tourism destinations. This understanding has been captured, in summary form, in the model of Destination Competitiveness/Sustainability (Ritchie and Crouch, 2003). This model contains seven (7) components which we have found to play a major role, from a policy perspective, in determining the competitiveness/sustainability of a tourism destination. In addition to the valuable understanding which these seven components provide from a policy perspective, the specific elements of each the major components provide a more useful/practical guidance to those who are responsible for the ongoing management of a DMO (Destination Management Organization). With this overview in mind, this paper will provide a detailed review and explanation of the model that I have developed with colleague, Dr. Geoffrey I. Crouch of Latrobe University in Melbourne, Australia. Based on previous presentations throughout the world, it has proven very helpful to both academics and practitioners who seek to understand the complex nature of tourism destination competitiveness/sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".